Import data¶

In [55]:
import pandas as pd
import datetime
import plotly.express as px
from plotly import graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats as st
In [4]:
transactions=pd.read_excel('KPMG_VI_New_rawdata_update_final.xlsx', sheet_name=1)
new_customers=pd.read_excel('KPMG_VI_New_rawdata_update_final.xlsx', sheet_name=2)
customer_demographic=pd.read_excel('KPMG_VI_New_rawdata_update_final.xlsx', sheet_name=3)
customer_address=pd.read_excel('KPMG_VI_New_rawdata_update_final.xlsx', sheet_name=4)

C:\Users\sam19\AppData\Local\Temp\ipykernel_11228\1701482122.py:2: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])
  new_customers=pd.read_excel('KPMG_VI_New_rawdata_update_final.xlsx', sheet_name=2)
C:\Users\sam19\AppData\Local\Temp\ipykernel_11228\1701482122.py:3: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])
  customer_demographic=pd.read_excel('KPMG_VI_New_rawdata_update_final.xlsx', sheet_name=3)

Processing data¶

In [5]:
transactions.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20000 entries, 0 to 19999
Data columns (total 13 columns):
 #   Column                   Non-Null Count  Dtype         
---  ------                   --------------  -----         
 0   transaction_id           20000 non-null  float64       
 1   product_id               20000 non-null  float64       
 2   customer_id              20000 non-null  float64       
 3   transaction_date         20000 non-null  datetime64[ns]
 4   online_order             19640 non-null  float64       
 5   order_status             20000 non-null  object        
 6   brand                    19803 non-null  object        
 7   product_line             19803 non-null  object        
 8   product_class            19803 non-null  object        
 9   product_size             19803 non-null  object        
 10  list_price               20000 non-null  float64       
 11  standard_cost            19803 non-null  float64       
 12  product_first_sold_date  19803 non-null  float64       
dtypes: datetime64[ns](1), float64(7), object(5)
memory usage: 2.0+ MB
In [6]:
# check brands
transactions['brand'].unique()
Out[6]:
array(['Solex', 'Trek Bicycles', 'OHM Cycles', 'Norco Bicycles',
       'Giant Bicycles', 'WeareA2B', nan], dtype=object)
In [7]:
# convert id to int format
for column in transactions.columns:
    if 'id' in column:
        transactions[column]=transactions[column].astype('int64')
In [8]:
# convert product_first_sold_date to correct format
transactions['product_first_sold_date']=pd.to_timedelta(transactions['product_first_sold_date'], unit='D') \
+ pd.to_datetime('1899-12-30')
In [9]:
# check unque names in columns
print(transactions['product_line'].unique())
print(transactions['product_class'].unique())
print(transactions['product_size'].unique())

['Standard' 'Road' 'Mountain' 'Touring' nan]
['medium' 'low' 'high' nan]
['medium' 'large' 'small' nan]
In [10]:
# check values in transaction table
transactions.describe()
Out[10]:
transaction_id product_id customer_id online_order list_price standard_cost
count 20000.000000 20000.00000 20000.000000 19640.000000 20000.000000 19803.000000
mean 10000.500000 45.36465 1738.246050 0.500458 1107.829449 556.046951
std 5773.647028 30.75359 1011.951046 0.500013 582.825242 405.955660
min 1.000000 0.00000 1.000000 0.000000 12.010000 7.210000
25% 5000.750000 18.00000 857.750000 0.000000 575.270000 215.140000
50% 10000.500000 44.00000 1736.000000 1.000000 1163.890000 507.580000
75% 15000.250000 72.00000 2613.000000 1.000000 1635.300000 795.100000
max 20000.000000 100.00000 5034.000000 1.000000 2091.470000 1759.850000

Prices vary from 12 to 2091 dollars, values seem to be realistic, no abnormality

In [11]:
# add margin columns to to the table in order to check the most profitable options
transactions['margin']=transactions['list_price']-transactions['standard_cost']
In [12]:
customer_demographic.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4000 entries, 0 to 3999
Data columns (total 13 columns):
 #   Column                               Non-Null Count  Dtype         
---  ------                               --------------  -----         
 0   customer_id                          4000 non-null   float64       
 1   first_name                           4000 non-null   object        
 2   last_name                            3875 non-null   object        
 3   gender                               4000 non-null   object        
 4   past_3_years_bike_related_purchases  4000 non-null   float64       
 5   DOB                                  3913 non-null   datetime64[ns]
 6   job_title                            3494 non-null   object        
 7   job_industry_category                3344 non-null   object        
 8   wealth_segment                       4000 non-null   object        
 9   deceased_indicator                   4000 non-null   object        
 10  default                              3698 non-null   object        
 11  owns_car                             4000 non-null   object        
 12  tenure                               3913 non-null   float64       
dtypes: datetime64[ns](1), float64(3), object(9)
memory usage: 406.4+ KB
In [13]:
# convert float values to int
for column in customer_demographic.columns:
    if customer_demographic[column].dtype=='float64':
        customer_demographic[column]=customer_demographic[column].astype('int64', errors='ignore')
In [14]:
# Unify gender column
customer_demographic['gender']=(
    customer_demographic['gender']
    .replace('F', 'Female')
    .replace('M', 'Male')
    .replace('Femal', 'Female'))
In [15]:
customer_demographic['today']=datetime.datetime.today()
In [16]:
# calculate users age for the current date
customer_demographic['age']=(customer_demographic['today']-customer_demographic['DOB']).apply(lambda x: x.days/365)
In [17]:
try:
    customer_demographic_new['age']=round(customer_demographic_new['age'])
except:
    pass
In [18]:
customer_demographic.describe()
Out[18]:
customer_id past_3_years_bike_related_purchases tenure age
count 4000.000000 4000.000000 3913.000000 3913.000000
mean 2000.500000 48.890000 10.657041 46.131513
std 1154.844867 28.715005 5.660146 12.804141
min 1.000000 0.000000 1.000000 21.454795
25% 1000.750000 24.000000 6.000000 36.495890
50% 2000.500000 48.000000 11.000000 46.106849
75% 3000.250000 73.000000 15.000000 55.610959
max 4000.000000 99.000000 22.000000 179.780822

There is user with age 179 years old, that's obviously mistake data, let's see distribtion of age users

In [19]:
fig = plt.figure()
customer_demographic['age'].hist(bins=50)
plt.title('Age distribution')
plt.show()

The main part of users are younger than 70 years, there are few abnormal values more than 80 years

In [20]:
# exclude users which are older 90 years old
customer_demographic=customer_demographic.query('age<90')
In [21]:
new_customers.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 23 columns):
 #   Column                               Non-Null Count  Dtype         
---  ------                               --------------  -----         
 0   first_name                           1000 non-null   object        
 1   last_name                            971 non-null    object        
 2   gender                               1000 non-null   object        
 3   past_3_years_bike_related_purchases  1000 non-null   int64         
 4   DOB                                  983 non-null    datetime64[ns]
 5   job_title                            894 non-null    object        
 6   job_industry_category                835 non-null    object        
 7   wealth_segment                       1000 non-null   object        
 8   deceased_indicator                   1000 non-null   object        
 9   owns_car                             1000 non-null   object        
 10  tenure                               1000 non-null   float64       
 11  address                              1000 non-null   object        
 12  postcode                             1000 non-null   float64       
 13  state                                1000 non-null   object        
 14  country                              1000 non-null   object        
 15  property_valuation                   1000 non-null   float64       
 16  Unnamed: 16                          1000 non-null   float64       
 17  Unnamed: 17                          1000 non-null   float64       
 18  Unnamed: 18                          1000 non-null   float64       
 19  Unnamed: 19                          1000 non-null   float64       
 20  Unnamed: 20                          1000 non-null   int64         
 21  Rank                                 1000 non-null   float64       
 22  Value                                1000 non-null   float64       
dtypes: datetime64[ns](1), float64(9), int64(2), object(11)
memory usage: 179.8+ KB
In [22]:
new_customers['today']=datetime.datetime.today()
In [23]:
# calculate age column for new users
new_customers['age']=(new_customers['today']-new_customers['DOB']).apply(lambda x: x.days/365)
In [24]:
try:
    new_customers['age']=round(new_customers['age'])
except:
    pass
In [25]:
# creat function for dividing age to age groups
def age_group(age):
    if age>21 and age<=35:
        return '21-35'
    if age>35 and age<=50:
        return '36-50'
    if age>50 and age<=75:
        return '50-75'
    else:
        return '>75'
In [26]:
# apply age function
new_customers['age_group']=new_customers['age'].apply(age_group)
In [27]:
customer_address.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3999 entries, 0 to 3998
Data columns (total 6 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   customer_id         3999 non-null   float64
 1   address             3999 non-null   object 
 2   postcode            3999 non-null   float64
 3   state               3999 non-null   object 
 4   country             3999 non-null   object 
 5   property_valuation  3999 non-null   float64
dtypes: float64(3), object(3)
memory usage: 187.6+ KB
In [28]:
# check unique state values
customer_address['state'].unique()
Out[28]:
array(['New South Wales', 'QLD', 'VIC', 'NSW', 'Victoria'], dtype=object)
In [29]:
# unify state values
customer_address['state']=(
    customer_address['state']
    .replace('QLD', 'Queensland')
    .replace('VIC', 'Victoria')
    .replace('NSW', 'New South Wales'))
In [30]:
# join demografic table with customer address table
customer_demographic_new=customer_demographic.merge(customer_address, on='customer_id', how='left')
In [31]:
# apply age function
customer_demographic_new['age_group']=customer_demographic_new['age'].apply(age_group)
In [32]:
customer_demographic_new.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3911 entries, 0 to 3910
Data columns (total 21 columns):
 #   Column                               Non-Null Count  Dtype         
---  ------                               --------------  -----         
 0   customer_id                          3911 non-null   int64         
 1   first_name                           3911 non-null   object        
 2   last_name                            3790 non-null   object        
 3   gender                               3911 non-null   object        
 4   past_3_years_bike_related_purchases  3911 non-null   int64         
 5   DOB                                  3911 non-null   datetime64[ns]
 6   job_title                            3414 non-null   object        
 7   job_industry_category                3255 non-null   object        
 8   wealth_segment                       3911 non-null   object        
 9   deceased_indicator                   3911 non-null   object        
 10  default                              3697 non-null   object        
 11  owns_car                             3911 non-null   object        
 12  tenure                               3911 non-null   float64       
 13  today                                3911 non-null   datetime64[ns]
 14  age                                  3911 non-null   float64       
 15  address                              3907 non-null   object        
 16  postcode                             3907 non-null   float64       
 17  state                                3907 non-null   object        
 18  country                              3907 non-null   object        
 19  property_valuation                   3907 non-null   float64       
 20  age_group                            3911 non-null   object        
dtypes: datetime64[ns](2), float64(4), int64(2), object(13)
memory usage: 672.2+ KB
In [33]:
# check distribution of users in age groups
customer_demographic_new['age_group'].value_counts()
Out[33]:
36-50    1646
50-75    1398
21-35     863
>75         4
Name: age_group, dtype: int64

The data is distributed consistently, the main part of users is between 36-75 years old

In [34]:
# join transactions info to custumers info
transactions_new=transactions.merge(customer_demographic_new, on='customer_id', how='left')
In [35]:
transactions_new['age_group'].value_counts()
Out[35]:
36-50    8282
50-75    6879
21-35    4351
>75        20
Name: age_group, dtype: int64
In [36]:
transactions_new.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 20000 entries, 0 to 19999
Data columns (total 34 columns):
 #   Column                               Non-Null Count  Dtype         
---  ------                               --------------  -----         
 0   transaction_id                       20000 non-null  int64         
 1   product_id                           20000 non-null  int64         
 2   customer_id                          20000 non-null  int64         
 3   transaction_date                     20000 non-null  datetime64[ns]
 4   online_order                         19640 non-null  float64       
 5   order_status                         20000 non-null  object        
 6   brand                                19803 non-null  object        
 7   product_line                         19803 non-null  object        
 8   product_class                        19803 non-null  object        
 9   product_size                         19803 non-null  object        
 10  list_price                           20000 non-null  float64       
 11  standard_cost                        19803 non-null  float64       
 12  product_first_sold_date              19803 non-null  datetime64[ns]
 13  margin                               19803 non-null  float64       
 14  first_name                           19532 non-null  object        
 15  last_name                            18906 non-null  object        
 16  gender                               19532 non-null  object        
 17  past_3_years_bike_related_purchases  19532 non-null  float64       
 18  DOB                                  19532 non-null  datetime64[ns]
 19  job_title                            17177 non-null  object        
 20  job_industry_category                16303 non-null  object        
 21  wealth_segment                       19532 non-null  object        
 22  deceased_indicator                   19532 non-null  object        
 23  default                              18536 non-null  object        
 24  owns_car                             19532 non-null  object        
 25  tenure                               19532 non-null  float64       
 26  today                                19532 non-null  datetime64[ns]
 27  age                                  19532 non-null  float64       
 28  address                              19503 non-null  object        
 29  postcode                             19503 non-null  float64       
 30  state                                19503 non-null  object        
 31  country                              19503 non-null  object        
 32  property_valuation                   19503 non-null  float64       
 33  age_group                            19532 non-null  object        
dtypes: datetime64[ns](4), float64(9), int64(3), object(18)
memory usage: 5.3+ MB

Transactions among age groups are distributed according to the amount of users in each group

Exploratory data analysis¶

In [37]:
# create function for grouping data
def grouped_data(param, value, func1, func2=None):
    pivot=transactions_new.groupby(param)[value].agg([func1, func2])
    return pivot
In [38]:
# check how median list price depends on age group and gender
grouped_gender=grouped_data(['age_group', 'gender'],'list_price', 'count', 'median').reset_index()
In [39]:
grouped_gender
Out[39]:
age_group gender count median
0 21-35 Female 2185 1163.89
1 21-35 Male 2166 1151.96
2 36-50 Female 4229 1148.64
3 36-50 Male 4053 1172.78
4 50-75 Female 3589 1163.89
5 50-75 Male 3290 1163.89
6 >75 Female 8 979.34
7 >75 Male 12 1209.49
In [40]:
fig= px.bar(grouped_gender, x='count', y='age_group', color='gender', text='median')
fig.update_layout(title='Distribution of age group',
                   xaxis_title='Number of transactions',
                   yaxis_title='',
                  barmode='stack',
                 yaxis={'categoryorder':'total ascending'}, title_x=0.5)
# fig.add_trace(go.Bar(x = labels, y = values - values2, text=values - values2, textposition='outside'))
fig.show()

On average, expenses in each age group are the same. Number of men and women in most groups is the same, users are older 75 years men, and their amount is not high

In [41]:
# chech how median list price depends on job industry category
grouped_industry=grouped_data('job_industry_category','list_price', 'sum', 'median')
In [42]:
grouped_industry.sort_values(by='sum', ascending=False)
Out[42]:
sum median
job_industry_category
Manufacturing 4422166.41 1163.89
Financial Services 4288265.20 1163.89
Health 3387936.62 1151.96
Retail 1968148.98 1198.46
Property 1436242.47 1151.96
Entertainment 784287.64 1172.78
IT 774993.82 1172.78
Argiculture 617353.97 1071.23
Telecommunications 375236.00 1148.64
In [43]:
fig= px.bar(grouped_industry, x='sum', y=grouped_industry.index, color=grouped_industry.index, text='median')
fig.update_layout(title='Distribution of median price',
                   xaxis_title='Number of transactions',
                   yaxis_title='',
                  barmode='stack',
                 yaxis={'categoryorder':'total ascending'}, title_x=0.5)
fig.show()

On average, median list price for all industry categories is almost the same, users with manufacturing and financial occupations spent more than other users

In [44]:
# check how wealth segments are distributed depending on occupation
grouped_wealth=grouped_data(['job_industry_category','wealth_segment'], 'list_price', 'count', 'median').reset_index()
In [45]:
fig= px.bar(grouped_wealth, x='count', y='job_industry_category', color='wealth_segment', text='median')
fig.update_layout(title='Distribution of wealth_segment',
                   xaxis_title='Number of transactions',
                   yaxis_title='',
                  barmode='stack',
                 yaxis={'categoryorder':'total ascending'}, title_x=0.5)
fig.show()
In [46]:
fig = px.histogram(grouped_wealth, 
             x='count',
             y='job_industry_category',
             barnorm = "percent",
             color='wealth_segment',
            color_discrete_sequence=px.colors.qualitative.Set3
            )
fig.update_layout(title='Wealth segment distribution',
                   xaxis_title='Доля заведений',
                   yaxis_title='', title_x=0.5,     
)
fig.show()

Median list price is almost the same for all wealth segments, main part of users is mass customers, the highest part of transactions made by affluent customers is in manufactiring and financial categories

In [47]:
# check users distribution among states
grouped_state=grouped_data(['state'], 'list_price', 'count', 'median')
In [48]:
grouped_state
Out[48]:
count median
state
New South Wales 10435 1151.96
Queensland 4173 1163.89
Victoria 4895 1172.78

The biggest part of transactions were made by users from New South Wales. Median price is also almost the same.

In [49]:
grouped_brand=grouped_data(['brand', 'product_class'], 'list_price', 'count', 'median').reset_index()
grouped_brand
Out[49]:
brand product_class count median
0 Giant Bicycles high 785 1179.00
1 Giant Bicycles low 196 590.26
2 Giant Bicycles medium 2331 1403.50
3 Norco Bicycles high 566 1148.64
4 Norco Bicycles low 632 688.63
5 Norco Bicycles medium 1712 1036.59
6 OHM Cycles high 771 227.88
7 OHM Cycles low 609 1073.07
8 OHM Cycles medium 1663 912.52
9 Solex high 566 1024.66
10 Solex low 410 945.04
11 Solex medium 3277 1071.23
12 Trek Bicycles high 325 495.72
13 Trek Bicycles low 786 980.37
14 Trek Bicycles medium 1879 1469.44
15 WeareA2B low 331 642.31
16 WeareA2B medium 2964 1362.99
In [50]:
fig= px.bar(grouped_brand, x='count', y='brand', color='product_class', text='median')
fig.update_layout(title='Distribution of brands',
                   xaxis_title='Number of transactions',
                   yaxis_title='',
                  barmode='stack',
                 yaxis={'categoryorder':'total ascending'}, title_x=0.5)
fig.show()
In [51]:
grouped_data(['brand'], 'margin', 'count', 'median')
Out[51]:
count median
brand
Giant Bicycles 3312 448.68
Norco Bicycles 2910 182.81
OHM Cycles 3043 199.10
Solex 4253 437.46
Trek Bicycles 2990 195.34
WeareA2B 3295 834.94

The most popular brand is Solex, but brand Giant Bicycles and WeareA2B provide the highest median price. Brand WeareA2B has the highest median margin

Based on conducted analysis we can assume that target users are from New South Wales with occupation in finance, manufacturing and health. The brands which should be offered are Giant Bicycles and WeareA2B, age group 35-75 years

According to analysis we conducted, we can see that median price doesn't depend on wealth segment, however, it's obvious that wealth users may provide more incomes for the company.

In [52]:
new=(new_customers
 .query('state=="NSW" and (job_industry_category=="Manufacturing" or job_industry_category=="Financial Services")'))
In [53]:
new.query('age>=36 and age<=75')
Out[53]:
first_name last_name gender past_3_years_bike_related_purchases DOB job_title job_industry_category wealth_segment deceased_indicator owns_car ... Unnamed: 16 Unnamed: 17 Unnamed: 18 Unnamed: 19 Unnamed: 20 Rank Value today age age_group
4 Melinda Hadlee Female 34 1965-09-21 Financial Analyst Financial Services Affluent Customer N No ... 0.93 0.9300 1.162500 1.162500 4 4.0 1.703125 2023-08-19 10:13:28.845287 58.0 50-75
6 Rutledge Hallt Male 23 1976-10-06 Compensation Analyst Financial Services Mass Customer N No ... 0.53 0.5300 0.530000 0.450500 6 6.0 1.671875 2023-08-19 10:13:28.845287 47.0 36-50
8 Duff Karlowicz Male 50 1972-04-28 Speech Pathologist Manufacturing Mass Customer N Yes ... 0.68 0.8500 1.062500 0.903125 8 8.0 1.656250 2023-08-19 10:13:28.845287 51.0 50-75
15 Dukie Swire Male 88 1954-03-31 NaN Manufacturing Affluent Customer N Yes ... 0.93 1.1625 1.453125 1.453125 16 16.0 1.562500 2023-08-19 10:13:28.845287 69.0 50-75
19 Karly Willavize Female 2 1954-08-12 Internal Auditor Manufacturing High Net Worth N No ... 1.06 1.0600 1.325000 1.325000 19 19.0 1.531250 2023-08-19 10:13:28.845287 69.0 50-75
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
960 Sonia Dunstall Female 0 1975-07-30 Accountant III Financial Services Mass Customer N No ... 0.41 0.4100 0.512500 0.435625 960 960.0 0.442000 2023-08-19 10:13:28.845287 48.0 36-50
967 Tillie Bisseker Female 74 1960-08-10 Civil Engineer Manufacturing High Net Worth N No ... 0.95 0.9500 1.187500 1.187500 967 967.0 0.435625 2023-08-19 10:13:28.845287 63.0 50-75
969 Evered Gludor Male 3 1951-04-30 Electrical Engineer Manufacturing High Net Worth N Yes ... 0.72 0.9000 0.900000 0.900000 967 967.0 0.435625 2023-08-19 10:13:28.845287 72.0 50-75
993 Weidar Etheridge Male 38 1959-07-13 Compensation Analyst Financial Services Mass Customer N Yes ... 0.48 0.6000 0.600000 0.510000 994 994.0 0.382500 2023-08-19 10:13:28.845287 64.0 50-75
995 Ferdinand Romanetti Male 60 1959-10-07 Paralegal Financial Services Affluent Customer N No ... 0.84 0.8400 0.840000 0.840000 996 996.0 0.374000 2023-08-19 10:13:28.845287 64.0 50-75

124 rows × 26 columns

There are 124 new customers which can be considered as target. As brands Giant Bicycles and WeareA2B can provide the highest profit they can be promoted more intensively. At the same time it should be considered that some actions are required for improving sales among wealth customers

In [56]:
# Check hypotises that affluent customer spend more than mass customer
# H0: Expenses Affluent and mass customers are the same
# H1: Affluent customers spend more

sample_1=transactions_new.loc[transactions_new['wealth_segment']=='Affluent Customer', 'list_price']
sample_2=transactions_new.loc[transactions_new['wealth_segment']!='Mass Customer', 'list_price']

alpha = 0.05

results=st.ttest_ind(sample_1, sample_2, equal_var = False)

print('p-value:', results.pvalue)


if results.pvalue < alpha:
    print("reject the null hypothesis")
else:
    print("don't reject the null hypothesis") 

p-value: 0.9531481272581976
don't reject the null hypothesis

As it was assumed earlier there is no significant difference in expenses between affluent and mass customers, so one of the task of future actions should be development of buisness with rich customers

Summary¶

During the research the following actions have been conducted:

  • conversion data to correct formats
  • unification data in gender and state columns
  • users are older 90 years have been excluded
  • age and age group columns have been added
  • margin column has been added

During the research the following conslusions were established:

  • On average, expenses in each age group are the same. Number of men and women in most groups is the same, users are older 75 years men, and their amount is not high
  • On average, median list price for users in all industry categories is almost the same, users with manufacturing and financial occupations spent slightly more than other users
  • Median list price is almost the same for users in all wealth segments, main part of users is mass customers, the highest part of transactions made by affluent customers is in manufactiring and financial categories
  • The biggest part of transactions were made by users from New South Wales. Median price is also almost the same
  • The most popular brand is Solex, but brand Giant Bicycles and WeareA2B provide the highest median price. Brand WeareA2B has the highest median margin
  • Based on conducted analysis we can assume that target users are from New South Wales with occupation in finance, manufacturing and health. The brands which should be offered are Giant Bicycles and WeareA2B, users age group 35-75 years
  • According to analysis we conducted, we can see that median price doesn't depend on wealth segment, however, it's obvious that wealth users may provide more incomes for the company
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